Face Recognition Algorithm Integrating Multimodal Information

As a key component of biometric technology, facial recognition plays a vital role in security monitoring, identity authentication and other fields. However, traditional single-modality face recognition algorithms are easily affected by various factors such as lighting conditions and occlusions, resulting in a significant decrease in recognition accuracy. To this end, this paper first introduces the research background and significance of multimodal face recognition algorithms, and analyzes the shortcomings of current research. Subsequently, a multimodal face recognition algorithm integrating deep learning is proposed, and the key steps such as feature extraction, modal fusion and classification recognition are elaborated in detail. Through experimental verification, the multimodal face recognition algorithm proposed in this paper has a recognition accuracy of roughly above 0.87 in complex scenarios such as lighting changes, diverse postures, and occlusion interference, and its robustness is also significantly enhanced.